getRhythmStats {limorhyde2} | R Documentation |
Compute rhythm statistics from fitted models
Description
This function uses stats::optim()
to compute various properties of
fitted curves with respect to time, potentially in each condition and for
each posterior sample, and adjusting for any covariates.
Usage
getRhythmStats(
fit,
fitType = c("posterior_mean", "posterior_samples", "raw"),
features = NULL,
dopar = TRUE,
rms = FALSE
)
Arguments
fit |
A |
fitType |
String indicating which fitted models to use to compute the
rhythmic statistics. A typical analysis using |
features |
Vector of names, row numbers, or logical values for
subsetting the features. |
dopar |
Logical indicating whether to run calculations in parallel if
a parallel backend is already set up, e.g., using
|
rms |
Logical indicating whether to calculate |
Value
A data.table
containing the following rhythm statistics:
-
peak_phase
: time between 0 andfit$period
at which the peak or maximum value occurs -
peak_value
-
trough_phase
: time between 0 andfit$period
at which the trough or minimum value occurs -
trough_value
-
peak_trough_amp
:peak_value - trough_value
-
rms_amp
: root mean square difference between fitted curve and mean value between time 0 andfit$period
(only calculated ifrms
isTRUE
) -
mesor
: mean value between time 0 andfit$period
The rows of the data.table
depend on the fit
object and fitType
:
-
fit
contains data from one condition andfitType
is posterior_mean' or 'raw': one row per feature. -
fit
contains data from one condition andfitType
is 'posterior_samples': one row per feature per posterior sample. -
fit
contains data from multiple conditions andfitType
is 'posterior_mean' or 'raw': one row per feature per condition. -
fit
contains data from multiple conditions andfitType
is 'posterior_samples': one row per feature per condition per posterior sample.
See Also
getModelFit()
, getPosteriorFit()
, getPosteriorSamples()
,
getDiffRhythmStats()
, getStatsIntervals()
Examples
library('data.table')
# rhythmicity in one condition
y = GSE54650$y
metadata = GSE54650$metadata
fit = getModelFit(y, metadata)
fit = getPosteriorFit(fit)
rhyStats = getRhythmStats(fit, features = c('13170', '13869'))
# rhythmicity and differential rhythmicity in multiple conditions
y = GSE34018$y
metadata = GSE34018$metadata
fit = getModelFit(y, metadata, nKnots = 3L, condColname = 'cond')
fit = getPosteriorFit(fit)
rhyStats = getRhythmStats(fit, features = c('13170', '12686'))
diffRhyStats = getDiffRhythmStats(fit, rhyStats)